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Computational Intelligence Applications for Text and Sentiment Data
Analysis explores the most recent advances in text information
processing and data analysis technologies, specifically focusing on
sentiment analysis from multifaceted data. The book investigates a
wide range of challenges involved in the accurate analysis of
online sentiments, including how to i) identify subjective
information from text, i.e., exclusion of ‘neutral’ or
‘factual’ comments that do not carry sentiment information, ii)
identify sentiment polarity, and iii) domain dependency. Spam and
fake news detection, short abbreviation, sarcasm, word negation,
and a lot of word ambiguity are also explored. Further chapters
look at the difficult process of extracting sentiment from
different multimodal information (audio, video and text), semantic
concepts. In each chapter, the book's authors explore how
computational intelligence (CI) techniques, such as deep learning,
convolutional neural network, fuzzy and rough set, global
optimizers, and hybrid machine learning techniques play an
important role in solving the inherent problems of sentiment
analysis applications.
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